According to Gartner, over 85% of customer interactions will be managed without a human by 2020.

We have seen a machine master the complex game of Go, previously thought to be the most difficult challenge of artificial processing. We have witnessed vehicles operating autonomously, including a caravan of trucks crossing Europe with only a single operator to monitor systems. We have seen a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks and all of this has given rise to a flurry of people claiming that the Artificial Intelligence revolution is already upon us.

However, while there is no doubt that there have been significant advancements in the field of AI, what we have seen is only a start on the path to what could be considered full AI.

Understanding the growth of artificial intelligence capabilities is crucial for understanding the advances we have seen. Full AI — that is to say complete, autonomous sentience — involves the ability for a machine to mimic a human to the point that it would be indistinguishable from them (the so-called Turing test). This type of true AI is still a long way from reality. Some would say the major constraint to the future development of AI is no longer our ability to develop the necessary algorithms but rather having the computing power to process the volume of data necessary to teach a machine to interpret complicated things like emotional responses. While it may be some time yet before we reach full AI, there will be much more practical applications of basic AI in the near term that hold the potential for significantly enhancing our lives.

With basic AI, the processing system — embedded within the appliance (local) or connected to a network (cloud) — learns and interprets responses based on “experience.” That experience comes in the form of training through using data sets that simulate the situations we want the system to learn from. This is the confluence of machine learning (ML) and AI. The capability to teach machines to interpret data is the key underpinning technology that will enable more complex forms of AI that can be autonomous in their responses to input. It is this type of AI that is getting the most attention. In the next ten years, the use of this kind of ML-based AI will likely fall into two categories:

Improvement and automation of daily life: Managing household tasks, self-driving cars and trucks, and the general automation of tasks that robots can perform significantly faster and more reliably than humans.

Exploration and development of new trends and insights: Artificial intelligence can help accelerate the rate discovery and science happening worldwide every day. The use of artificial intelligence to automate science and technology will drive our ability to discover new cures, technologies, tools, cells, planets, etc., ultimately pushing artificial intelligence itself to new heights.

There is no doubt about the commercial prospects for autonomous robotic systems in the commercial market for aspects such as online sales conversion, customer satisfaction, and operational efficiency. We see this application already being advanced to the point that it will become commercially viable, which is the first step to it becoming practical and widespread. Simply put, if revenue can be made from it, it will become self-sustaining and thus continue to grow. Amazon Echo, a personal assistant, has succeeded as a solidly commercial application of autonomous technology in the United States.

In addition to the automation of transportation and logistics, a wide variety of additional technologies that utilize autonomous processing techniques is being built. Currently, the artificial assistant or “chatbot” concept is one of the most popular. By creating the illusion of a fully sentient remote participant, it makes interaction with technology more approachable. There have been obvious failings of this technology (the unfiltered Microsoft chatbot “Tay” as a prime example), but the application of properly developed and managed artificial systems for interaction is an important step along the route to full AI. This is also a hugely important application of AI as it will bring technology to those who previously could not engage with technology completely for any number of physical or mental reasons. By making technology simpler and more human to interact with, you remove some of the barriers to its use that cause difficulty for people with various impairments.

The use of artificial intelligence for development and discovery is just now beginning to gain traction. But over the next decade, this will become an area of significant investment and development. There are so many repetitive tasks involved in any scientific or research project that using robotic intelligence engines to manage and perfect the more complex and repetitive tasks would greatly increase the speed at which new breakthroughs could be uncovered.

There is also the tantalizing possibility that as we increase the capability of our AI systems, they could actually perform research and discover new avenues to explore. While this is still a long way away, it could greatly accelerate the discoveries needed for many advancements that could improve and extend our lives.

The dystopian vision of robots assuming complete control of society is unlikely; the nuances of perception, intuition, and plain old “gut-check reactions” still elude machines. Learning from repetition, improving patterns, and developing new processes is well within reach of current AI models, and will strengthen in the coming years as advances in artificial intelligence — specifically machine learning and neural networks — continue. Rather than being frightened by the perceived threat of AI, it would be wise to embrace the possibilities that AI offers.

TrueSight is an AIOps platform, powered by machine learning and analytics, that elevates IT operations to address multi-cloud complexity and the speed of digital transformation.